Model Updating Technique Based on Modal Participation Factors for Beam Structures

Abstract

This article proposes a model updating technique based on modal participation factors for a beam structure. In this model updating technique, the error functions of the dynamic characteristic differences between measurement and model are generated as the number of modes under consideration and minimized using the multiobjective optimization techniques. A modal influence factor defined by modal participation factors for each mode is presented for the selection of the best solution from among Pareto solutions. The selection rule represented in this article makes it possible to reflect the contributions of each mode on the behavior of a structure. The model is updated using natural frequencies measured in an impact hammer test of a beam structure and the validity of the updated model is confirmed by the strain responses measured from the test. It is found that the bending stiffness of the beam structure as the parameter for model updating can be identified by the proposed techniques. Furthermore, through comparing the models updated by the simple sum model updating and the technique in this research, it is verified that the proposed technique is more appropriate for the model updating.

Identification of Wind Loads and Estimation of Structural Responses of Super‐Tall Buildings by an Inverse Method

“…Recently, structural health monitoring (SHM) of structural systems has received considerable attention in civil engineering community and some useful results for evaluating structural integrity, durability, and reliability throughout the lifecycle of structures have been obtained (Park et al., ; Qarib and Adeli, ; Park et al., ; Amezquita‐Sanchez and Adeli, , ). In practical applications of SHM, system identification exhibits a critical component and extensive research has been conducted in this field (Jiang and Adeli, ; Adeli and Jiang, ; Amezquita‐Sanchez and Adeli, ; Qarib and Adeli, ; Sun et al., ; An et al., ; Yuen and Mu, ; Zhang et al., ; Oh et al., ; Sun and Betti, ; Boscato et al., ; Bolourchi et al., ; Cha and Buyukozturk, ; Shabbir and Omenzetter, ; Vincenzi and Savoia, ). To evaluate the performance of Taipei 101 Tower under typhoon and earthquake actions, a monitoring system was installed in the super‐tall building.…”

Physics‐based search and optimization: Inspirations from nature

“…Parameter estimation and system identification have been active areas of research in recent years (Sun, Feng, Liu, & Feng, ; Yuen & Mu, ; Zhang & Zhou, ), which are used in the emerging area of structural health monitoring (Oh, Kim, & Park, ; Sun & Betti, ; Cha & Buyukozturk, ). Tang, Zhou, Xue, and Xie () used the BB–BC algorithm for parameter estimation of structural systems formulated as a multi‐modal optimization problem with high dimension.…”

“…Kaveh and Talatahari (2009a) employ a hybrid BB-BC optimization algorithm for optimal design of truss structures (Adeli & Kamal, 1986) and compare it to other optimization methods including GA, PSO (Boulkabeit, Mthembu, De Lima Neto, & Marwala, 2014), ant colony optimization (Chou & Pham, 2015), and harmony search algorithm (Siddique & Adeli, 2015a, and 2015c. Parameter estimation and system identification have been active areas of research in recent years (Sun, Feng, Liu, & Feng, 2015;Yuen & Mu, 2015;Zhang & Zhou, 2015), which are used in the emerging area of structural health monitoring (Oh, Kim, & Park, 2015;Sun & Betti, 2015;Cha & Buyukozturk, 2015). Tang, Zhou, Xue, and Xie (2010) used the BB-BC algorithm for parameter estimation of structural systems formulated as a multi-modal optimization problem with high dimension.…”

Seismic Data‐Driven Identification of Linear Models for Building Structures Using Performance and Stabilizing Objectives

“…Recently, representative St‐Id methods are iterative optimization dependent (García‐Palencia and Santini‐Bell, ; Sun et al., ; Xu et al., ) and further utilize computational intelligence (Bolourchi et al., ; Vincenzi and Savoia, ). In the broad area of health monitoring, the latest studies on model updating (Oh et al., ; Sun and Betti, ), signal processing (Qarib and Adeli, ), and damage detection (Cha and Buyukozturk, ) share the same characteristic of using intelligent optimization. Hence, the incorporation of computational intelligence seems to be a promising and effective choice for structural health monitoring, especially for large real‐life structures (Sirca and Adeli, ).…”